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bayespm

0.2.0

Bayesian Statistical Process Monitoring

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Overview

About
Maintained by Dimitrios KiagiasFirst published 2023-07-052 releasesCRAN page ↗

The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" doi:10.1080/00224065.2021.1916413, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" doi:10.1080/00224065.2022.2161434, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" doi:10.1080/00224065.2022.2161435.

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Health

CRAN checks
13OK
Slowest check: 3.3 min · r-devel-linux-x86_64-fedora-clang
Code health
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Tests · ratio 0.00
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Coverage
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  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

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References docs
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Dependencies

Declared dependencies
5 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5.0
Imports (8)
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none
Suggests (0)
none
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none
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Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 0.2.0Latest
    2023-09-11 · current release · diff ↗
  • 0.1.0
    2023-07-05
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-07-05
Total releases
2 / 3 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.5.0
Bundled data
0.5 KB / 2 files
Download size
48 KB
Installed size
not tracked yet
With dependencies
not tracked yet
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